Titlebook: New Frontiers in Bayesian Statistics; BAYSM 2021, Online, Raffaele Argiento,Federico Camerlenghi,Sally Pagan Conference proceedings 2022 T

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,Power-Expected-Posterior Methodology with?Baseline Shrinkage Priors,rior is updated using imaginary data. This work focuses on normal regression models when the number of observations . is smaller than the number of explanatory variables .. We introduce the PEP prior methodology using different baseline shrinkage priors and we perform some comparisons in simulated data sets.

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,Bayesian Nonparametric Scalar-on-Image Regression via?Potts-Gibbs Random Partition Models,ocess is spatially dependent, thereby encouraging groups representing spatially contiguous regions. In addition, Bayesian shrinkage priors are utilised to identify the covariates and regions that are most relevant for the prediction. The proposed model is illustrated using the simulated data sets.

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,A Bayesian Nonparametric Test for?Cross-Group Differences Relative to?a?Control,up distributions are modeled in a flexible way using a dependent Dirichlet process. Monte Carlo experiments suggest that our proposal performs better than state-of-the-art frequentist alternatives for small sample sizes.

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,Block Structured Graph Priors in?Gaussian Graphical Models,arlo Markov chain that avoids any . normalizing constant calculation when comparing graphical models. The novelty of this procedure is that it looks for block structured graphs, hence proposing moves that add or remove not just a single link but an entire group of them.

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Conference proceedings 2022The book is intended for a broad audience of people interested in statistics, and provides a series of stimulating contributions on theoretical, methodological, and computational aspects of Bayesian statistics..